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Abstract

In this thesis we propose a normative approach to market microstructure analysis. We study, model, and quantify low-level high-frequency interactions among agents in financial markets. This is an environment where electronic agents are much better positioned to both make decisions and take actions because of the amount of information and the rapid pace of activity, which overwhelm humans. Unlike previous work in this area, we are not only interested in explaining why microstructure variables (prices, volumes, spreads, order flow, etc) behave in a certain way, but also in determining optimal policies for agents interacting in this environment. Our prescriptive ?as opposed to explanatory ?method treats market interactions as a stochastic control problem. We suggest a quantitative framework for solving this problem, describe a reinforcement learning algorithm tailored to this domain, and conduct empirical studies on very large datasets of high-frequency data. We hope that our research will lead not just to automation of market activities, but to more orderly and efficient financial markets.